Extract the profit data for Tesla and GameStop and build a dashboard to compare the price of the stock vs the profit for the hedge fund¶

Question 1: Use yfinance to Extract Stock Data¶

In [6]:
!pip install yfinance==0.2.4

Requirement already satisfied: yfinance==0.2.4 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (0.2.4)
Requirement already satisfied: pandas>=1.3.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.2.4) (1.3.5)
Requirement already satisfied: numpy>=1.16.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.2.4) (1.21.6)
Requirement already satisfied: requests>=2.26 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.2.4) (2.29.0)
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In [7]:
!pip install pandas==1.3.3

Collecting pandas==1.3.3
  Downloading pandas-1.3.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.3 MB)
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Installing collected packages: pandas
  Attempting uninstall: pandas
    Found existing installation: pandas 1.3.5
    Uninstalling pandas-1.3.5:
      Successfully uninstalled pandas-1.3.5
Successfully installed pandas-1.3.3
In [8]:
import pandas as pd
import yfinance as yf
In [9]:
tesla = yf.Ticker("TSLA")
In [10]:
tesla_data = pd.DataFrame(tesla.history(period="max"))
In [11]:
tesla_data.head()
Out[11]:
Open High Low Close Volume Dividends Stock Splits
Date
2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 281494500 0.0 0.0
2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 257806500 0.0 0.0
2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 123282000 0.0 0.0
2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 77097000 0.0 0.0
2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 103003500 0.0 0.0
In [12]:
tesla_data.reset_index(inplace=True)
In [13]:
tesla_data
Out[13]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 281494500 0.0 0.0
1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 257806500 0.0 0.0
2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 123282000 0.0 0.0
3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 77097000 0.0 0.0
4 2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 103003500 0.0 0.0
... ... ... ... ... ... ... ... ...
3290 2023-07-26 00:00:00-04:00 263.250000 268.040009 261.750000 264.350006 95856200 0.0 0.0
3291 2023-07-27 00:00:00-04:00 268.309998 269.130005 255.300003 255.710007 103697300 0.0 0.0
3292 2023-07-28 00:00:00-04:00 259.859985 267.250000 258.230011 266.440002 111446000 0.0 0.0
3293 2023-07-31 00:00:00-04:00 267.480011 269.079987 263.779999 267.429993 84582200 0.0 0.0
3294 2023-08-01 00:00:00-04:00 266.260010 266.470001 260.250000 261.070007 83166000 0.0 0.0

3295 rows × 8 columns

In [17]:
tesla_data.head()
Out[17]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 281494500 0.0 0.0
1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 257806500 0.0 0.0
2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 123282000 0.0 0.0
3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 77097000 0.0 0.0
4 2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 103003500 0.0 0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data¶¶

In [14]:
tesla_url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm'
In [15]:
!pip install requests==2.26.0

Collecting requests==2.26.0
  Downloading requests-2.26.0-py2.py3-none-any.whl (62 kB)
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Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests==2.26.0) (1.26.15)
Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests==2.26.0) (2023.5.7)
Collecting charset-normalizer~=2.0.0 (from requests==2.26.0)
  Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB)
Requirement already satisfied: idna<4,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests==2.26.0) (3.4)
Installing collected packages: charset-normalizer, requests
  Attempting uninstall: charset-normalizer
    Found existing installation: charset-normalizer 3.1.0
    Uninstalling charset-normalizer-3.1.0:
      Successfully uninstalled charset-normalizer-3.1.0
  Attempting uninstall: requests
    Found existing installation: requests 2.29.0
    Uninstalling requests-2.29.0:
      Successfully uninstalled requests-2.29.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
ibm-cos-sdk-core 2.13.0 requires requests<3.0,>=2.28.1, but you have requests 2.26.0 which is incompatible.
Successfully installed charset-normalizer-2.0.12 requests-2.26.0
In [16]:
!mamba install bs4==4.10.0 -y

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        mamba (1.4.2) supported by @QuantStack

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Looking for: ['bs4==4.10.0']

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Pinned packages:
  - python 3.7.*


Transaction

  Prefix: /home/jupyterlab/conda/envs/python

  Updating specs:

   - bs4==4.10.0
   - ca-certificates
   - certifi
   - openssl


  Package               Version  Build         Channel                 Size
─────────────────────────────────────────────────────────────────────────────
  Install:
─────────────────────────────────────────────────────────────────────────────

  + bs4                  4.10.0  hd3eb1b0_0    pkgs/main/noarch        10kB

  Upgrade:
─────────────────────────────────────────────────────────────────────────────

  - ca-certificates    2023.5.7  hbcca054_0    conda-forge                 
  + ca-certificates  2023.05.30  h06a4308_0    pkgs/main/linux-64     123kB
  - openssl              1.1.1t  h0b41bf4_0    conda-forge                 
  + openssl              1.1.1u  h7f8727e_0    pkgs/main/linux-64       4MB

  Downgrade:
─────────────────────────────────────────────────────────────────────────────

  - beautifulsoup4       4.11.1  pyha770c72_0  conda-forge                 
  + beautifulsoup4       4.10.0  pyh06a4308_0  pkgs/main/noarch        87kB

  Summary:

  Install: 1 packages
  Upgrade: 2 packages
  Downgrade: 1 packages

  Total download: 4MB

─────────────────────────────────────────────────────────────────────────────


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Preparing transaction: done
Verifying transaction: done
Executing transaction: done
In [17]:
!mamba install html5lib==1.1 -y 
!pip install lxml==4.6.4

                  __    __    __    __
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                /    \/    \/    \/    \
███████████████/  /██/  /██/  /██/  /████████████████████████
              /  / \   / \   / \   / \  \____
             /  /   \_/   \_/   \_/   \    o \__,
            / _/                       \_____/  `
            |/
        ███╗   ███╗ █████╗ ███╗   ███╗██████╗  █████╗
        ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗
        ██╔████╔██║███████║██╔████╔██║██████╔╝███████║
        ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║
        ██║ ╚═╝ ██║██║  ██║██║ ╚═╝ ██║██████╔╝██║  ██║
        ╚═╝     ╚═╝╚═╝  ╚═╝╚═╝     ╚═╝╚═════╝ ╚═╝  ╚═╝

        mamba (1.4.2) supported by @QuantStack

        GitHub:  https://github.com/mamba-org/mamba
        Twitter: https://twitter.com/QuantStack

█████████████████████████████████████████████████████████████


Looking for: ['html5lib==1.1']

pkgs/main/linux-64                                          Using cache
pkgs/main/noarch                                            Using cache
pkgs/r/linux-64                                             Using cache
pkgs/r/noarch                                               Using cache

Pinned packages:
  - python 3.7.*


Transaction

  Prefix: /home/jupyterlab/conda/envs/python

  Updating specs:

   - html5lib==1.1
   - ca-certificates
   - certifi
   - openssl


  Package         Version  Build         Channel                Size
──────────────────────────────────────────────────────────────────────
  Install:
──────────────────────────────────────────────────────────────────────

  + html5lib          1.1  pyhd3eb1b0_0  pkgs/main/noarch       93kB
  + webencodings    0.5.1  py37_1        pkgs/main/linux-64     20kB

  Summary:

  Install: 2 packages

  Total download: 113kB

──────────────────────────────────────────────────────────────────────


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Downloading and Extracting Packages

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Collecting lxml==4.6.4
  Downloading lxml-4.6.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.3 MB)
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Installing collected packages: lxml
  Attempting uninstall: lxml
    Found existing installation: lxml 4.9.2
    Uninstalling lxml-4.9.2:
      Successfully uninstalled lxml-4.9.2
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
yfinance 0.2.4 requires beautifulsoup4>=4.11.1, but you have beautifulsoup4 4.10.0 which is incompatible.
yfinance 0.2.4 requires lxml>=4.9.1, but you have lxml 4.6.4 which is incompatible.
Successfully installed lxml-4.6.4
In [18]:
import requests
from bs4 import BeautifulSoup
In [19]:
r=requests.get(tesla_url).text
In [20]:
soup = BeautifulSoup(r, 'html5lib')
In [21]:
Tesla_revenue = pd.DataFrame(columns = ["Date", "Revenue"])
table=soup.find_all("tbody")[1]
for row in table.find_all('tr'):
    col = row.find_all('td')
    if (col != []):
        date = col[0].text
        revenue = col[1].text
        Tesla_revenue = Tesla_revenue.append({"Date":date, "Revenue":revenue}, ignore_index=True)
In [22]:
Tesla_revenue["Revenue"] = Tesla_revenue['Revenue'].str.replace(',|\$',"")

/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version.
  """Entry point for launching an IPython kernel.
In [23]:
Tesla_revenue
Out[23]:
Date Revenue
0 2022-09-30 21454
1 2022-06-30 16934
2 2022-03-31 18756
3 2021-12-31 17719
4 2021-09-30 13757
5 2021-06-30 11958
6 2021-03-31 10389
7 2020-12-31 10744
8 2020-09-30 8771
9 2020-06-30 6036
10 2020-03-31 5985
11 2019-12-31 7384
12 2019-09-30 6303
13 2019-06-30 6350
14 2019-03-31 4541
15 2018-12-31 7226
16 2018-09-30 6824
17 2018-06-30 4002
18 2018-03-31 3409
19 2017-12-31 3288
20 2017-09-30 2985
21 2017-06-30 2790
22 2017-03-31 2696
23 2016-12-31 2285
24 2016-09-30 2298
25 2016-06-30 1270
26 2016-03-31 1147
27 2015-12-31 1214
28 2015-09-30 937
29 2015-06-30 955
30 2015-03-31 940
31 2014-12-31 957
32 2014-09-30 852
33 2014-06-30 769
34 2014-03-31 621
35 2013-12-31 615
36 2013-09-30 431
37 2013-06-30 405
38 2013-03-31 562
39 2012-12-31 306
40 2012-09-30 50
41 2012-06-30 27
42 2012-03-31 30
43 2011-12-31 39
44 2011-09-30 58
45 2011-06-30 58
46 2011-03-31 49
47 2010-12-31 36
48 2010-09-30 31
49 2010-06-30 28
50 2010-03-31 21
51 2009-12-31
52 2009-09-30 46
53 2009-06-30 27
In [24]:
Tesla_revenue.dropna(inplace=True)

Tesla_revenue = Tesla_revenue[Tesla_revenue['Revenue'] != ""]
In [25]:
Tesla_revenue.tail()
Out[25]:
Date Revenue
48 2010-09-30 31
49 2010-06-30 28
50 2010-03-31 21
52 2009-09-30 46
53 2009-06-30 27

Question 3: Use yfinance to Extract Stock Data¶

In [26]:
Gamestop = yf.Ticker("GME")
In [27]:
Gamestop_data = Gamestop.history(period="max")
In [28]:
Gamestop_data.reset_index(inplace=True)
In [29]:
Gme_data = pd.DataFrame(Gamestop_data)
In [30]:
Gme_data.head()
Out[30]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 00:00:00-05:00 1.620128 1.693350 1.603296 1.691667 76216000 0.0 0.0
1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683250 11021600 0.0 0.0
2 2002-02-15 00:00:00-05:00 1.683251 1.687459 1.658002 1.674834 8389600 0.0 0.0
3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0

Question 4: Use Webscraping to Extract GME Revenue Data¶

In [31]:
Html_data = ' https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html'
In [32]:
data  = requests.get(Html_data).text
In [33]:
soup2 = BeautifulSoup(data, 'html5lib')
In [34]:
Gme_revenue = pd.DataFrame(columns = ["Date", "Revenue"])
table=soup2.find_all("tbody")[1]
for row in table.find_all('tr'):
    col = row.find_all('td')
    if (col != []):
        date = col[0].text
        revenue = col[1].text
        Gme_revenue = Gme_revenue.append({"Date":date, "Revenue":revenue}, ignore_index=True)
In [35]:
Gme_revenue["Revenue"] = Gme_revenue['Revenue'].str.replace(',|\$',"")

/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version.
  """Entry point for launching an IPython kernel.
In [36]:
Gme_revenue.tail()
Out[36]:
Date Revenue
57 2006-01-31 1667
58 2005-10-31 534
59 2005-07-31 416
60 2005-04-30 475
61 2005-01-31 709

Question 5: Plot Tesla Stock Graph¶

In [37]:
!pip install plotly==5.3.1

Collecting plotly==5.3.1
  Downloading plotly-5.3.1-py2.py3-none-any.whl (23.9 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 23.9/23.9 MB 41.8 MB/s eta 0:00:0000:0100:01
Requirement already satisfied: tenacity>=6.2.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from plotly==5.3.1) (8.2.2)
Requirement already satisfied: six in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from plotly==5.3.1) (1.16.0)
Installing collected packages: plotly
  Attempting uninstall: plotly
    Found existing installation: plotly 5.14.1
    Uninstalling plotly-5.14.1:
      Successfully uninstalled plotly-5.14.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
dash 2.9.3 requires dash-core-components==2.0.0, which is not installed.
dash 2.9.3 requires dash-html-components==2.0.0, which is not installed.
dash 2.9.3 requires dash-table==5.0.0, which is not installed.
Successfully installed plotly-5.3.1
In [38]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
In [39]:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [40]:
make_graph(tesla_data, Tesla_revenue, 'tesla')

Question 6: Plot GameStop Stock Graph¶

In [41]:
make_graph(Gme_data, Gme_revenue, 'GameStop')
In [ ]: